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Full-Text Articles in Life Sciences
Modeling The Daily Activities Of Breeding Colonial Seabirds: Dynamic Occupancy Patterns In Multiple Habitat Patches, Andrea L. Moore, Smruti P. Damania, Shandelle M. Henson, James L. Hayward
Modeling The Daily Activities Of Breeding Colonial Seabirds: Dynamic Occupancy Patterns In Multiple Habitat Patches, Andrea L. Moore, Smruti P. Damania, Shandelle M. Henson, James L. Hayward
Faculty Publications
We constructed differential equation models for the diurnal abundance and distribution of breeding glaucous-winged gulls (Larus glaucescens) as they moved among nesting and non-nesting habitat patches. We used time scale techniques to reduce the differential equations to algebraic equations and connected the models to field data. The models explained the data as a function of abiotic environmental variables with R2=0.57. A primary goal of this study is to demonstrate the utility of a methodology that can be used by ecologists and wildlife managers to understand and predict daily activity patterns in breeding seabirds.
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) Ii: A Continuous-Time Model, Smruti P. Damania, Karl W. Phillips, Shandelle M. Henson, James L. Hayward
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) Ii: A Continuous-Time Model, Smruti P. Damania, Karl W. Phillips, Shandelle M. Henson, James L. Hayward
Faculty Publications
The diurnal distribution and abundance dynamics of loafing Glaucous-winged Gulls (Larus glaucescens) were examined at Protection Island National Wildlife Refuge, Strait of Juan de Fuca, Washington. Asynchronous movement of gulls among three habitat patches dedicated to loafing was modeled as a function of environmental variables using differential equations. Multiple time scale analysis led to the derivation of algebraic models for habitat patch occupancy dynamics. The models were parameterized with hourly census data collected from each habitat patch, and the resulting model predictions were compared with observed census data. A four-compartment model explained 41% of the variability in the data. Models …